Arc Additive Manufacturing Process Research Articles

On selecting proper process parameters for cold metal transfer (CMT)–based wire arc additive manufacturing (WAAM) process

On selecting proper process parameters for cold metal transfer (CMT)–based wire arc additive manufacturing (WAAM) process

Development of an indirect measurement method for the Contact Tube to Workpiece Distance (CTDW) in the Direct Energy Deposition – Arc (DED-ARC) process for different arc types

During the layer-by-layer build-up in the Direct Energy Deposition (DED) - Arc additive manufacturing (AM) process, the distance between the contact tube and the workpiece, effectively the welded layer, changes. Since the weld paths are predefined by the path planning software, a constant Contact Tube to Workpiece Distance (CTWD) and weld bead height is assumed. However, even small changes in geometry, such as crossovers of weld paths, result in higher weld beads than assumed. Similarly, an incorrectly assumed bead height as input to the path planning will result in a change in the CTWD. The sum of the deviations of the real weld geometries from the assumed ones in the path planning can greatly influence the CTWD. This implies that the dimensional accuracy may be significantly compromised. This research presents an approach for a general indirect measurement method using the welding current to obtain the CTWD during the actual welding process. A real-time process control method is implemented and validated using the mechanically controlled short arc and the pulsed arc process. Varying process parameters are used to validate the general applicability for a specific material. For the mechanically controlled short arc process, the model underestimates the measured CTWD by a mean error of 3.4 mm. The pulse process is overestimated by a mean error of 2.2 mm. The standard deviation for the pulse process with 1.3 mm is slightly smaller than for the short arc process with 1.7 mm.

Numerical simulation and experimental investigation of temperature distribution during the wire arc additive manufacturing (WAAM) process

Numerical simulation and experimental investigation of temperature distribution during the wire arc additive manufacturing (WAAM) process

Investigating the Forming Characteristics of 316 Stainless Steel Fabricated through Cold Metal Transfer (CMT) Wire and Arc Additive Manufacturing.

Wire and arc additive manufacturing (WAAM), recognized for its capability to fabricate large-scale, complex parts, stands out due to its significant deposition rates and cost-effectiveness, positioning it as a forward-looking manufacturing method. In this research, we employed two welding currents to produce samples of 316 austenitic stainless steel utilizing the Cold Metal Transfer wire arc additive manufacturing process (CMT-WAAM). This study initially evaluated the maximum allowable arc travel speed (MAWFS) and the formation characteristics of the deposition bead, considering deposition currents that vary between 100 A and175 A in both CMT and CMT pulse(CMT+P) modes. Thereafter, the effect of the CMT+P mode arc on the microstructure evolution was analyzed using the EBSD technique. The findings indicate that the arc travel speed and deposition current significantly affect the deposition bead's dimensions. Specifically, an increase in travel speed or a reduction in current results in reduced bead width and height. Moreover, the employment of the CMT+P arc mode led to a reduction in the average grain size in the mid-section of the sample fabricated by CMT arc and wire additive manufacturing, from 13.426 μm to 9.429 μm. Therefore, the components of 316 stainless steel produced through the CMT+P-WAAM method are considered fit for industrial applications.

Correlation between pulse frequencies, microstructure and mechanical properties of TA15 in wire and arc additive manufacturing

Wire and arc additive manufacturing (WAAM) has been developed to be a highly efficient technique for making large Ti-alloy products. However, the deposition of metal by this method causes coarse texture, which limits its application. To address this, the study utilizes a wire and pulsed arc additive manufacturing (WPAAM) process for making TA15 alloy. Compared with the WAAM method, this WPAAM method inducts several current/voltage-impulse cycles under different pulse frequencies, which makes grains tiny and improves tensile strength. The study then uses a VOF model to discuss the effect of impulse cycles on the solidification process. The findings suggested that the impulse cycles deform the molten pool and influence the vibration in the solid/liquid interface. This makes the coarse columnar grains tiny and forms diminutive α laths. This paper further discusses the correlation between the microstructure and the mechanical properties. The results show that the CBGs and the α laths play a joint contribution to yield strength in the WAAMed TA15 alloy. This study provides a reference for optimizing the microstructure of WAAM titanium alloys by adjusting pulse frequencies and provides a theoretical basis for strengthening WAAMed Ti-alloy.

In situ process monitoring of multi-layer deposition in wire arc additive manufacturing (WAAM) process with acoustic data analysis and machine learning

In situ process monitoring of multi-layer deposition in wire arc additive manufacturing (WAAM) process with acoustic data analysis and machine learning

Benchmarking the quality improvement strategies of wire arc additive manufacturing process using fuzzy QFD approach

PurposeThe purpose of this paper is to improve the productivity and quality of the wire arc additive manufacturing process by benchmarking the strategies from the selected six strategies, namely, heat treatment process, inter pass cooling process, inter pass cold rolling process, peening process, friction stir processing and oscillation process.Design/methodology/approachTo overcome the lack of certainty associated with correlations and relationships in quality functional deployment, fuzzy numbers have been integrated with the quality functional deployment framework. Twenty performance measures have been identified from the literature under five groups, namely, mechanical properties, physical properties, geometrical properties, cost and material properties. Using house of quality weights are allocated to performance measures and groups, relationships are established between performance measures and strategies, and correlations are assigned between strategies. Finally, for each strategy, relative importance, score and crisp values are calculated.FindingsInter pass cold rolling process strategy is computed with the highest crisp value of 15.80 which is followed by peening process, heat treatment process, friction stir processing, inter pass cooling process,] and oscillation process strategy.Originality/valueTo the best of the authors’ knowledge, there has been no research in the literature that analyzes the strategies to improve the quality and productivity of the wire arc additive manufacturing process.

A New Particle Swarm Optimization Algorithm for Outlier Detection: Industrial Data Clustering in Wire Arc Additive Manufacturing

In this paper, a novel outlier detection method is proposed for industrial data analysis based on the fuzzy C-means (FCM) algorithm. An adaptive switching randomly perturbed particle swarm optimization algorithm (ASRPPSO) is put forward to optimize the initial cluster centroids of the FCM algorithm. The superiority of the proposed ASRPPSO is demonstrated over five existing PSO algorithms on a series of benchmark functions. To illustrate its application potential, the proposed ASRPPSO-based FCM algorithm is exploited in the outlier detection problem for analyzing the real-world industrial data collected from a wire arc additive manufacturing pilot line in Sweden. Experimental results demonstrate that the proposed ASRPPSO-based FCM algorithm outperforms the standard FCM algorithm in detecting outliers of real-world industrial data. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Electric arc (which is governed by the current and arc voltage) plays a significant role in monitoring the operating status of the wire arc additive manufacturing (WAAM) process. The nominal periodic current and voltage may occasionally change abruptly due to anomalies (such as arc instability, unstable metal transfer, geometrical deviations, and surface contaminations), which would affect the quality of the fabricated component. This paper focuses on detecting possible anomalies by analyzing the current and voltage during the WAAM process. A novel clustering-based outlier detection method is proposed for anomaly detection where abnormal and normal instances are categorized into two separate clusters. A new particle swarm optimization algorithm is put forward to optimize the initial cluster centroid so as to improve the detection accuracy. The proposed outlier detection method is applied to real-world data collected from a WAAM pilot line for detecting abnormal instances. Experimental results demonstrate the effectiveness of the proposed outlier detection method. The proposed outlier detection method can be applied to other industrial applications including electrical engineering, mechanical engineering and medical engineering. In the future, we aim to develop an online outlier detection system based on the proposed method for real-time for anomaly detection and defect prediction.

Double pulsed current adopted in local dry underwater WAAM to simultaneously enhanced strength and ductility of 308 L multi-layer component

The double pulsed current waveform modulation was applied to address the severe challenges of greatly declined strength and ductility caused by continuous heat accumulation and water cooling in local dry underwater wire arc additive manufacturing (LDU-WAAM) process. The microstructures and mechanical performance of 308 L stainless steel multi-layer components prepared by LDU-WAAM using single pulsed waveform (SPW) and double pulsed waveform (DPW) were comprehensively investigated in this work. The results demonstrate that the microstructures of LDU-WAAM parts were both composed of abundant columnar crystals and few equiaxed crystals. Extremely directional columnar crystals dominated in each deposited layer centre prepared by SPW, while the weakened growth tendency of columnar crystal and uniform microstructure were achieved by DPW. DPW exhibited outstanding stirring action in LDU-WAAM process, which was conducive to the refined grain, improved liquidity of deposited metal, increased ferrite content, dislocation density, and austenite deformed twinning content compared with the SPW multi-layer component. Hence, the average microhardness, ultimate tensile strength, and elongation of DPW deposited layers were improved to 204.7 HV, 659.2 MPa, and 24.9 %, respectively, which enhanced by 10.1 %, 12.5 %, and 37.5 % than that of SPW sample. The research results provide a new technique optimization method to simultaneous enhance the strength and ductility of LDU-WAAM process and promote the landing application of LDU-WAAM in underwater on-site repair.

Evolutions of microstructure and mechanical property of 308L stainless steel repaired by the local dry underwater wire arc additive manufacturing

Local dry underwater wire arc additive manufacturing process (LDU-WAAM) was innovatively proposed to repair the damaged large underwater structures. A thorough investigation was performed to explore the microstructure and mechanical properties of 308L stainless steel multi-layer components prepared by LDU-WAAM technique. The results demonstrate that the microstructure of all components was composed of a significant amount of austenite and a minor amount of ferrite. Subjected to the repeated action of local arc heating and rapid water cooling, the multi-layer components were deposited into a stratified structure, each layer was mainly composed plenty of directional columnar crystals, and the adjacent layers were separated by a small number of equiaxed crystals. With the increase of deposited layer counts, the heat transfer paths from deposited metal to the substrate were gradually lengthened, which increased average grain size, LAGBs proportion, dislocation density, and austenite deformed twinning content of the multi-layer sample, also decreased its ferrite content. Hence, the microstructure of LDU-WAAM multi-layer component varied significantly from the bottom to the top, which led to the deteriorated mechanical properties along the building direction. In the 3-layer component, the ultimate tensile strengths of the first, second, and third layers were 554 MPa, 532 MPa, and 519 MPa, respectively. And the elongations of that were 25.3%, 23.2%, and 19.5%, respectively. The research results verify the feasibility of local dry underwater wire arc additive manufacturing process and provide a new process method for underwater on-site repair.

Bead geometry prediction and optimization for corner structures in directed energy deposition using machine learning

Wire Arc Additive Manufacturing as a Directed Energy Deposition technology has disruptive potential for modern manufacturing. The technology comes with the flexibility and material efficiency of additive manufacturing processes while mitigating the disadvantages through high material deposition rates and high energy efficiency. However, the prevalence of the technology is inhibited by its geometrical inaccuracy and the large induced residual stresses. This work tackles the former problem by capturing the process parameter-geometry relationship using Machine Learning. To do so, multiple mild steel welding beads with varying shape features like corner angles are printed using a Metal Inert Gas welding machine attached to an industrial robot. The cross-sectional profile of the printed beads is measured using a point laser sensor and correlated through a Multilayer Perceptron to input features such as travel speed, wire feed speed, and shape features. By incorporating varying corner angles, a holistic model, not limited to geometry prediction of straight beads only, is trained. Using the model, excess material at the inner angle of corners determined by the overlapping regions of the two adjacent beads can be predicted. By generating a database of possible bead shapes an inverse algorithm that suggests welding parameter combinations that yield a smooth bead shape at the corner is created, thus enabling improvement of the overall accuracy of the Wire Arc Additive Manufacturing process.

Effects of Contaminations on Electric Arc Behavior and Occurrence of Defects in Wire Arc Additive Manufacturing of 316L-Si Stainless Steel

Additive Manufacturing is a manufacturing process that consists of obtaining a three-dimensional object from the deposition of material layer by layer, unlike conventional subtractive manufacturing methods. Wire Arc Additive Manufacturing stands out for its high productivity among the Additive Manufacturing technologies for manufacturing metal parts. On the other hand, the excessive heat input promotes increased residual stress levels and the occurrence of defects, such as pores, voids, a lack of fusion, and delamination. These defects result in abnormalities during the process, such as disturbances in electrical responses. Therefore, process monitoring and the detection of defects and failures in manufactured items are of fundamental importance to ensure product quality and certify the high productivity characteristic of this process. Thus, this work aimed to characterize the effects of different contaminations on the electric arc behavior of the Wire Arc Additive Manufacturing process and the occurrence of microscopic defects in thin walls manufactured by this process. To investigate the presence of defects in the metal preforms, experimental conditions were used to promote the appearance of defects, such as the insertion of contaminants. To accomplish the electric arc behavior analysis, voltage and current temporal data were represented through histograms and cyclograms, and the arc stability was assessed based on the Vilarinho index for a short circuit. Effectively, the introduction of contaminants caused electric arc disturbances that led to the appearance of manufacturing defects, such as inclusions and porosities, observed through metallographic characterization. The results confirm that the introduction of contaminations could be identified early in the Wire Arc Additive Manufacturing process through electric arc data analysis.

Study on the Arc Additive Manufacturing Process and Mechanical Properties of Inconel 625 Nickel-Based Alloy Bulk

_ The melt-inert gas-shielded arc additive manufacturing experiment of Inconel 625 nickel-based alloy bulk was carried out. The influence of current mode, current, travel speed, weld bead spacing, deposition path, and arc length on the formation of overlapping weld beads was studied. The incomplete fusion near the bottom weld toe of adjacent weld beads was the main defect. The optimized parameters for bulk additive manufacturing obtained from the experiment were as follows: pulse current of 70 A, travel speed of 200 mm/min, weld bead spacing of 3 mm, dry extension of 10 mm, continuous deposition, and arc length of 1 mm. Due to the thermal influence during the deposition of adjacent weld beads, directional grains were formed inside the weld bead and at the boundary with adjacent weld beads. However, the direction of grain growth was not uniform throughout the entire weld bead, and a consistent texture of the entire weld bead was not formed. The tensile strengths of the specimens in the X, Y, and Z directions were 858.5, 838.7, and 827.2 MPa, respectively, and the fracture elongations were 53.3%, 37.6%, and 42.1%, respectively. The strength in the Z direction, which is the vertical direction, was the lowest. Keywords arc additive manufacturing; nickel-based alloy; incomplete fusion; texture

Microstructure and mechanical properties of Mg–Li alloys fabricated by wire arc additive manufacturing

Gas tungsten arc welding (GTAW) based wire arc additive manufacturing (WAAM) process is first used to fabricate a thin wall of LA103Z magnesium-lithium alloy. Results indicate that the grain size of the LA103Z thin-walled bottom region significantly differs from that of the middle and top regions. The main phases present in the LA103Z thin wall include α-Mg and β-Li phases, along with some particulate second phases (LiAl, Li2MgAl). Moreover, the thermal action of the subsequent sedimentary layer promotes the transformation of β-Li→α-Mg+β-Li and Li2MgAl →LiAl phase. The mechanical properties of the LA103Z thin wall are affected by a combination of factors such as grain size, the proportion of the α-Mg phase, and morphology. The tensile strength of the LA103Z thin-walled samples in the bottom, middle, and top regions is measured at 195.1 MPa, 181.8 MPa, and 201.4 MPa, respectively, with corresponding elongation percentages of 3.4%, 7.3%, and 12.4%. In the building direction, the tensile strength is recorded as 177.7 MPa, with an elongation percentage of 7%. The study reveals that WAAM-GTAW has the potential to make magnesium-lithium alloys more widely available.

Computational welding simulation of a plasma wire arc additive manufacturing process for high-strength steel

Computational welding simulation of a plasma wire arc additive manufacturing process for high-strength steel

Optimal data-driven control of manufacturing processes using reinforcement learning: an application to wire arc additive manufacturing

AbstractNowadays, artificial intelligence (AI) has become a crucial Key Enabling Technology with extensive application in diverse industrial sectors. Recently, considerable focus has been directed towards utilizing AI for the development of optimal control in industrial processes. In particular, reinforcement learning (RL) techniques have made significant advancements, enabling their application to data-driven problem-solving for the control of complex systems. Since industrial manufacturing processes can be treated as MIMO non-linear systems, RL can be used to develop complex data-driven intelligent decision-making or control systems. In this work, the workflow for developing a RL application for industrial manufacturing processes, including reward function setup, development of reduced order models and control policy construction, is addressed, and a new process-based reward function is proposed. To showcase the proposed approach, a case study is developed with reference to a wire arc additive manufacturing (WAAM) process. Based on experimental tests, a Reduced Order Model of the system is obtained and a Deep Deterministic Policy Gradient Controller is trained with aim to produce a simple geometry. Particular attention is given to the sim-to-real process by developing a WAAM simulator which allows to simulate the process in a realistic environment and to generate the code to be deployed on the motion platform controller.

A metamodel of the wire arc additive manufacturing process based on basis spline entities

A metamodel of the wire arc additive manufacturing process based on basis spline entities

Phase-field modeling and Experimental investigation for rapid solidification in wire and arc additive manufacturing

The rapid solidification speed and significant temperature gradient observed in the wire and arc additive manufacturing (WAAM) process result in a deviation of the solid-liquid interface from the equilibrium state assumed by the conventional directional solidification phase field (PF) model. This, in turn, limits the ability of the traditional directional solidification PF model to accurately depict the microstructure evolution in additive manufacturing, where non-equilibrium effects dominate. In this work, we employed a 3D finite element (FE) model to simulate the temperature distribution during the wire and arc additive manufacturing (WAAM) process. Additionally, a modified PF model that applies to additive manufacturing is also used to simulate the dendrite morphology and solute segregation behavior in the WAAM process at different scanning speeds. Experimental validation of the simulation results was also conducted. We observed a significant reduction in dendrite growth velocity and solute segregation with increasing scanning speed, attributable to the combined effects of the temperature gradient G(t) and solidification speed Vp. Furthermore, higher scanning speeds were found to increase the primary dendrite arm spacing (PDAS) and average dendrite width. Importantly, our results demonstrated a quantitative agreement between the simulation results using the modified directional solidification PF model and the experimental observations. This approach provides a foundation for selecting and predicting processing parameters and microstructural characteristics under additive manufacturing conditions, facilitating a comprehensive analysis of dendrite growth and solute segregation at different scanning speeds in the WAAM process.

An economic evaluation of hybrid WAAM-subtractive manufacturing in relation to deposition process parameters

Wire Arc Additive Manufacturing (WAAM) processes could offer significant advantages, in terms of energy and material use efficiency, when used for the production and repair of metal components. However, although a large number of economic assessments have been carried out on powder-based Additive Manufacturing (AM) processes, few studies have addressed the cost of WAAM technologies. There is a lack of research that has simultaneously considered the economic and environmental impacts of hybrid processes based on WAAM. In this study, a cost model, adapted to Cold Metal Transfer (CMT), has been implemented on a steel airfoil mock-up selected as a case study. All the cost drivers have been identified at the different stages of the process, together with their relationships to the deposition process parameters, which have been systematically varied to assess their influence. The results show a close correlation between the overall cost trend and the cumulative energy demand as the process parameters vary, and that the optimisation of the main CMT process parameters should primarily be aimed at increasing the material deposition efficiency, which bridges the CMT and finishing processes.

Avaliação da Influência dos Parâmetros de Processamento na Manufatura Aditiva do Aço VP50IM por PCGTAW

Abstract The wire arc additive manufacturing process or WAAM (Wire Additive Arc Welding) is recognized as a process able of making pieces of high geometric complexity, with mechanical properties comparable to those of the cast material. However, there are significant challenges associated with WAAM, such as undesirable microstructures and mechanical properties, high residual stresses and geometric distortion. This study aims to contribute to the selection of deposition parameters for VP50IM steel using WAAM via pulsed TIG (Tungsten Inert Gas) and characterization of the generated stacking, using the Central Composite Complete methodology, CCC. In this study, the peak (Cp) and base (Cb) current, wire feed speed during peak (Vap), base (Vab) and welding speed (Vs) were varied. The ideal parameter presented was Cp=200A, Cb=100A, Vap=2.9cm/min, Vab=1.2cm/min and Vs=20cm/min. Tensile tests showed up to 15% greater resistance in the samples in the longitudinal section in the welding direction compared to the transverse direction. Hardness tests demonstrated up to 9% less hardness at the center of the stack compared to the top and bottom. The fracture analysis of the specimens showed ductile fracture.